From the photography aficionado type digital cameras to the high-end computer vision systems, digital imaging is a fast growing technology that is becoming an integral part of everyday life. In its most basic definition, a digital image is a computer readable representation of an image of a subject taken by a digital imaging device, e.g. a camera, video camera, or the like. A computer readable representation, or digital image, typically includes a number of pixels arranged in an image file or document according to one of many available graphic formats. For example, some graphic file formats include, without limitation, bitmap, Graphics Interchange Format (GIF), Joint Photographic Experts Group (JPEG) format, and the like. A subject is anything that can be imaged, i.e. photographed, video taped, or the like. In general, a subject may be an object or part thereof, a person or a part thereof, a scenic view, an animal, or the like. An image of a subject typically comprises viewing conditions that, to some extent, make the image unique. In imaging, viewing conditions typically refer to the relative orientation between the camera and the object (i.e., the pose), and the external illumination under which the images are acquired.
Variation in viewing conditions have long been a challenge in the field of image clustering for computer vision. Particularly, clustering of three-dimensional (3-D) images according to the subject represented presents a difficult problem because images of the same subject under different viewing conditions can be drastically different. Conversely, images with similar appearance may originate from two very different subjects. The various viewing conditions can be isolated to present a different problem for clustering purposes. One such problem subset can be the clustering problem for images taken under varying illumination conditions with the subject in fixed pose. This problem is difficult because images of the same subject may look drastically different under different lighting, while different subjects may appear similar under different illumination conditions.
Consider for example the images shown in FIG. 1. There are two natural ways to consider clustering these images: they can be clustered by illumination condition or by identity of the subject. It should be noted that in each such cluster, the shadow formation is more or less the same, and this can be exploited directly by computing some statistics among pixels. Numerous algorithms for estimating lighting direction have been proposed and undoubtedly many of these algorithms can be applied with few modifications to clustering according to lighting. On the other hand, clustering by identity is considerably more difficult when the appearances of a subject class vary dramatically. For example, prior work on face recognition has observed that the appearance variation of the same person under different lighting condition is almost always larger than the appearance variation of different people under the same lighting conditions.
Therefore, it is desirable to provide a system and method that can reliably cluster digital images of subjects taken under various illumination conditions based on the subject's identity.